From Research Software to Compiler and Runtime Engineering
My roadmap is built around one connected objective: developing the systems depth required to build reliable, high-performance software from research infrastructure to language runtimes. Research software, C/C++ systems development, ML infrastructure, high-performance computing, and compiler engineering are not separate destinations. They are progressive layers of the same technical path.
One Career Path, Four Connected Role Areas
Each role family strengthens the next. The progression preserves a clear technical identity while allowing me to contribute immediately through research software, systems development, and ML infrastructure.
Research Software Engineer
Scientific software, reproducible experiments, data and model pipelines, HPC execution, metrics, automation, documentation, testing, and research collaboration.
Supported by doctoral research and research toolingSystems Software Engineer
C/C++, memory, file systems, processes, debugging, Linux, concurrency, build systems, modular interfaces, correctness, reliability, and performance.
Strengthened through structured systems projectsML Systems & AI Infrastructure
Training workflows, experiment orchestration, distributed execution, model evaluation, artifact management, reproducibility, infrastructure automation, and performance-aware ML systems.
Connected through GenCyberSynth, HPC, and MetricForgeCompiler & Runtime Engineer
Parsing, intermediate representations, optimization, code generation, virtual machines, execution models, runtime memory, garbage collection, concurrency, and low-level performance.
Destination built upon deep systems expertiseTechnical Development Timeline
The roadmap progresses from fundamentals to increasingly complex systems. Each phase produces working software, documentation, tests, measurable results, and stronger engineering judgment.
C, Memory, Data Structures, and Low-Level Reasoning
Build disciplined command of how data is represented, passed, allocated, modified, validated, and released. The objective is not syntax memorization, but reliable reasoning about program behavior.
Core Knowledge
- C language semantics
- Arrays and pointers
- Dynamic memory
- Structs and modular interfaces
- Data structures and algorithms
Engineering Practice
- Safe input handling
- Defensive validation
- Unit and integration testing
- GDB and Valgrind
- Compiler warnings and sanitizers
Current Projects
- C Systems Mastery
- Arrays and Pointers Lab
- C Foundations Toolkit
- Reusable utility libraries
- Make-based project workflows
Linux, Processes, Files, Concurrency, and Tooling
Move from isolated programs to software that interacts directly with the operating system, processes real inputs, manages resources, and behaves predictably under failure.
Core Knowledge
- Files and streams
- Processes and system calls
- Signals and pipes
- Threads and synchronization
- POSIX interfaces
Engineering Practice
- CLI interface design
- Error propagation
- Resource ownership
- Build automation
- Cross-platform verification
Project Evidence
- C CLI Lab
- Unix-style command implementations
- File-processing tools
- Process-oriented utilities
- Testing and documentation
C++ Libraries, Research Software, and ML Systems
Build reusable libraries and infrastructure that connect high-level research workflows with efficient low-level implementations, stable APIs, testing, packaging, and reproducibility.
Core Knowledge
- Modern C++
- RAII and ownership
- Templates and generic design
- Library interfaces
- Python bindings
Infrastructure Skills
- CMake and packaging
- Model-training pipelines
- HPC and SLURM
- Artifact tracking
- Metrics and reproducibility
Project Evidence
- MetricForge
- GenCyberSynth
- C++ core and Python API
- Research automation
- Paper-grade reporting
Languages, Parsing, IRs, Virtual Machines, and Code Generation
Apply systems foundations to language implementation. Build complete compiler and interpreter components rather than only studying them theoretically.
Frontend
- Lexical analysis
- Parsing strategies
- Abstract syntax trees
- Type systems
- Semantic analysis
Middle & Backend
- Intermediate representations
- Control-flow graphs
- Data-flow analysis
- Optimization passes
- Machine-code generation
Planned Systems
- Interpreter
- Bytecode virtual machine
- Small optimizing compiler
- LLVM-based experiments
- Compiler test infrastructure
Execution Systems, Memory Management, and Optimization
Specialize in the systems responsible for executing programs: runtime memory, scheduling, concurrency, garbage collection, JIT compilation, profiling, and hardware-conscious optimization.
Runtime Systems
- Execution state
- Stack and heap models
- Object representation
- Garbage collection
- Runtime services
Performance
- Profiling and benchmarking
- Cache-aware optimization
- Vectorization
- Parallel execution
- JIT compilation
Career Destination
- Compiler Engineer
- Runtime Engineer
- Performance Engineer
- VM Engineer
- Senior Systems Specialist
Projects as Proof of Progress
The roadmap is implementation-driven. Each project should produce working software, tests, examples, technical notes, reproducible builds, and a clear explanation of the engineering decisions.
C Systems Mastery
Progressive laboratories covering safe input, memory, arrays, pointers, modularity, data structures, debugging, and increasingly complex systems behavior.
Output: reusable modules, tests, Makefiles, notes, examples, and verified programs.C CLI Lab
Unix-inspired tools for developing practical understanding of arguments, streams, file operations, text processing, directories, processes, and POSIX conventions.
Output: professional command suite with documentation, tests, and shared libraries.MetricForge
Open-source ML metrics infrastructure with a C++ core, Python bindings, stable APIs, CMake builds, examples, tests, and education-oriented documentation.
Output: production-style library architecture and cross-language integration.GenCyberSynth
Research framework for model training, synthetic-data generation, HPC orchestration, evaluation, artifact management, experiment reproducibility, and publication reporting.
Output: research software evidence aligned with ML infrastructure and scientific computing roles.Continuous Depth Areas
These subjects run across every phase. They are not one-time courses; they deepen through repeated implementation, debugging, measurement, and design.
C & C++ Language Depth
Language semantics, ownership, object lifetime, templates, interfaces, undefined behavior, and implementation tradeoffs.
Computer Architecture
Instructions, registers, caches, memory hierarchy, pipelines, branch behavior, and hardware-software interaction.
Operating Systems
Processes, threads, scheduling, virtual memory, filesystems, synchronization, system calls, and resource management.
Algorithms & Data Structures
Implementation, complexity, memory behavior, graph analysis, compiler data structures, and performance-aware design.
Debugging & Reliability
Tests, sanitizers, debuggers, profiling, assertions, error handling, observability, and reproducible failure analysis.
Performance Engineering
Measurement, benchmarking, bottleneck analysis, cache behavior, parallelism, optimization, and evidence-based decisions.
Principles Guiding the Roadmap
Mastery Before Accumulation
Complete and understand projects deeply rather than collecting disconnected technologies or unfinished repositories.
Implementation Before Abstraction
Build internal mechanisms to understand how tools, libraries, runtimes, and infrastructure actually work.
Evidence Before Claims
Use tests, benchmarks, profiles, experiment artifacts, and reproducible results to support technical conclusions.
Direction Without False Expertise
Present the full trajectory confidently while distinguishing demonstrated capabilities from planned specialization.
Building Reliable Systems from Research to Runtime
The roadmap is intentionally demanding and cumulative. Research software provides immediate engineering value, C/C++ systems work builds low-level depth, ML infrastructure strengthens scalable execution, and compiler/runtime projects turn those foundations into long-term specialization.